Mastering Query Optimization Techniques for Modern Data Engineers

QUERY OPTIMIZATION
TECHNIQUES
A DEEP DIVE FOR MODERN DATA
ENGINEERS
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WHAT IS QUERY OPTIMIZATION?
• Query Optimization is the process of altering a query to
improve performance without changing the output.
• Focuses on reducing CPU usage, memory overhead, disk
I/O, and network costs.
• A critical component of ETL, ELT, batch processing, and real-
time pipelines.
• Plays a central role in tools like Apache Spark, Databricks,
Hive, Snowflake, BigQuery.
• Goal: Lower latency, cost, and resource usage for better
throughput.
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CORE CONCEPTS OF
OPTIMIZATION
• Logical Plan: The high-level intent
of your query.
• Physical Plan: Steps the engine
takes to execute the query.
• Execution Engine: Interprets the
physical plan and performs tasks.
• Statistics: Help cost-based
optimizers choose optimal paths.
• Caching & Materialization: Avoids
recomputation.

RULE-BASED OPTIMIZATION (RBO)
• Uses heuristics or fixed rules to
improve queries.
• Examples:
• Predicate pushdown
• Column pruning
• Rewriting joins
• Found in Spark, Hive, SQL
engines.

COST-BASED OPTIMIZATION
(CBO)
• Relies on statistics (row counts, distinct values, file sizes).
• Engine chooses least-cost path based on estimates.
• Found in Spark Catalyst, Presto, Snowflake, BigQuery.
• Requires ANALYZE TABLE or automatic stats collection.

COMMON MISTAKES TO AVOID
• SELECT * in production: Increases data transfer unnecessarily.
• Joining without filters or exploding joins: Leads to large shuffles and memory issues.
• Not using partitioning or using inappropriate partition keys: Results in full-table scans.
• No indexes (for SQL systems): Slows down queries on large tables.
• Over-caching, causing memory pressure: Spark jobs can fail or stall due to insufficient memory.
• Not collecting statistics: Prevents CBO from making optimal decisions.
• Ignoring data skew: Causes long task runtimes and imbalanced processing.
• Using large shuffle joins unnecessarily: Instead, use broadcast joins when feasible.
• Not monitoring jobs post-deployment: Missed opportunities for real-world tuning.
• Relying only on defaults: Default settings are not always optimal for big data workloads.

OPTIMIZATION TECHNIQUES IN
SPARK
• Use DataFrame API over RDDs for Catalyst optimization.
• Predicate Pushdown: filters early to reduce data scanned.
• Partition Pruning: leverages partition columns in filters.
• Broadcast Joins: for smaller tables (<10MB default).
• Use .persist() or .cache() only after shuffles.
Code Example:
from pyspark.sql.functions import broadcast
big_df.join(broadcast(small_df), "id")

ADVANCED SPARK OPTIMIZATIONS
• Z-Ordering (Databricks Delta): cluster data for fast filtering.
• Adaptive Query Execution (AQE): Spark 3+ auto-tunes joins.
• Vectorized Reader: enables faster Parquet/ORC reads.
• Coalesce vs Repartition: Optimize for number of tasks.
• Skew Handling: Use salting or skew hints.
• Bucketing: Organizes data to reduce shuffle during joins.
• Join Reordering: Catalyst reorders joins for cost efficiency.
• Dynamic Partition Pruning: Spark 3+ enables late pruning at
runtime.
• Avoid Cartesian Joins: Explicitly block them unless required.
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SNOWFLAKE AND BIGQUERY OPTIMIZATION TIPS
• Avoid nested subqueries unless needed.
• Use clustering keys (Snowflake) and partitioning (BQ).
• Materialize intermediate steps.
• Monitor via Query Profiler.
• Use LIMIT with heavy queries during testing.
• Compress and partition external tables for faster reads.
• Use approximate functions like APPROX_COUNT_DISTINCT
for large scans.
• Avoid SELECT *; specify columns to reduce read cost.
• Schedule stats collection regularly to support optimizer.
• Avoid repeated UDFs; rewrite logic using native SQL where
possible.
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REAL-TIME PIPELINE OPTIMIZATION (KAFKA + SPARK)
• Filter early in streaming jobs.
• Use watermarking + window aggregation wisely.
• Persist frequently accessed data.
• Write to compact formats like Delta or Parquet.
• Monitor lag, checkpoint size, backpressure.
• Use asynchronous writes and batch intervals.
• Reduce trigger intervals carefully to avoid micro-batching
delays.
• Avoid joins unless necessary; prefer enrichment via
dimension tables.
• Monitor file sizes and small file problems in sinks.
• Use schema evolution in Delta to support changes.

INTERVIEW QUESTIONS FOR DATA ENGINEERS
1.What’s the difference between logical and physical query plans?
• Logical plan represents what the query does. Physical plan shows how it is executed with actual steps like scans, joins,
filters.
2.How does Spark’s Catalyst Optimizer work?
• Catalyst uses a series of rule-based and cost-based transformations to optimize logical and physical plans.
3.When would you use broadcast join in Spark?
• When one of the tables is small enough to fit in memory (~10MB), to avoid shuffle and improve speed.
4.What happens if you over-cache in Spark?
• It leads to memory pressure, frequent garbage collection, and possible job failures.
5.How do you identify data skew in a pipeline?
• Using Spark UI: look for tasks that take significantly longer or have more input data than others.
6.Explain partition pruning with an example.
• When a query filter (e.g., WHERE year=2024) matches a partition column, only relevant partitions are read, improving
performance

PERFORMANCE MONITORING TOOLS
• Spark UI: DAG, task time, stage analysis
• Databricks Query Profile: I/O, compute time, skew
• EXPLAIN/EXPLAIN ANALYZE: SQL plan analysis
• CloudWatch/Grafana: Metrics + alerts
• Query Replay Tools (Snowflake)
• Azure Monitor + Log Analytics: For Databricks or
Synapse jobs
• DataDog: Application-level metrics and alerting
• Ganglia/Prometheus: Cluster resource tracking
• AWS Glue Job Metrics: Specific for Glue ETL
workloads
• Heap Size/Shuffle Read/Writes: Key metrics in
Spark UI to watch

BONUS: STORAGE FORMAT MATTERS
• Choose the right format:
• Parquet: columnar, best for analytics
• Delta: versioned, ACID
• ORC: optimized for Hive
• Columnar formats + compression = faster queries
• Avoid JSON/CSV in production unless necessary
• Use Snappy/ZSTD compression for efficient storage
• Consider file size: optimal file size for Spark is 100–
250MB
• Enable schema-on-read with formats like
Delta/Parquet
• Prefer immutable files and append-only operations
to minimize compaction
• Use merge-on-read only if frequent updates are
expected

LEARN OPTIMIZATION WITH ACCENTFUTURE
• Real-time case studies: slow pipelines → fast systems
• Work with Spark, Databricks, Presto, Snowflake
• Practice optimizations hands-on
• Learn to analyze job metrics
• Crack interviews with scenario-based training
• Explore end-to-end workflows: ingestion →
transformation → optimization
• Get mentorship from working professionals
• Participate in mock interviews & job prep sessions
• Free tools and notebooks provided for practice
• Certificate of completion and project support
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READY TO GET STARTED?
• Visit: www.accentfuture.com
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